Colin Hill is CEO and co-founder of GNS Healthcare, a Big Data analytics company that uses observational data to discover what works for whom in healthcare. Hill’s scientific background and expertise spans computational physics, systems biology, and personalized medicine. He is a frequent speaker at international scientific and industry conferences and has appeared in numerous publications and television segments including The Wall Street Journal, CNBC Morning Call, Nature, Wired, and The Economist. He serves on the advisory boards of the Boston Medical Center Philanthropic Trust and sickle cell anemia drug company AesRx. In 2004, Hill was named to MIT Technology Review’s TR100 list of the top innovators in the world under the age of 35. He earned master’s degrees in physics from McGill University and Cornell University.

In God We Trust, All Others Bring Evidence

Hello, I’m Carol McCall, Chief Strategy Officer for GNS Healthcare, and I recently participated in a Webinar conducted by Forbes’ Matthew Herper. Our panel’s focus was Real World Outcomes, which, like Big Data, is a hot topic in healthcare these days. It was an interesting discussion among diverse stakeholders about how Real World Outcomes can change healthcare.

I thought I would use this occasion to bring Colin into the discussion.

Carol: What does “Real World Outcomes” mean to you?

Colin: Basically it means using “real world data” – which is any data not generated from a randomized clinical trial (RCT) – to understand the effects of treatments on patient outcomes. By design, RCTs randomly determine who gets what treatment and control for other factors that could confound the outcome. Real World Outcomes means using data from everyday care to understand outcomes when you don’t control these things but simply observe them.

Every year, the U.S. runs a $2.7 trillion natural experiment that creates lots of observational data, everything from insurance claims and lab tests to patient registries and health IT systems. And we’re creating new kinds of data every day. Just look at the mHealth and Quantified Self movements, to say nothing of the data explosion in genomics, biology and personalized medicine. People often say that healthcare is becoming a Big Data industry, but they fail to note that most of this data is coming from the real world, not from RCTs. So we’d better get good at using real world data to help solve healthcare’s problems.

Carol: Why is Real World Outcomes important now?

Colin: The idea behind this is not new. Government agencies and private companies already use real world data to study the impact of treatments on outcomes. What has changed is the pressing economic need for it.

It’s well-accepted that today’s level of healthcare spending is unsustainable. In fact, economists project that if we do nothing, USA Inc. will go broke in the next decade. It’s also well-accepted that part of the solution lies in changing healthcare’s business models. Today, providers are paid based on the volume of care they deliver whether or not it produces a good outcome. Tomorrow, the goal is to pay for “value, not volume,” but paying for value means knowing which treatments work better and for whom. In other words, the business models will be fueled by evidence: profitability will depend on performance, and performance will require knowledge, much (or most) of which will be gained from the techniques of Real World Outcomes, rather than the few-and-far-between RCTs. Pretty soon our motto will be, “In God we trust; all others bring evidence.”

Carol: If the idea isn’t new, what’s different?

Colin: While the concept is the same, the practice of it will change dramatically. The challenge comes from doing all of this at scale. It comes down to the three Vs – volume, variety and velocity.

At the front end, the volume and variety of the data is obviously increasing and the developments you’re seeing around the new infrastructures for Big Data are addressing this. At the back end, the velocity at which companies want to use the data is increasing – they want evidence they can use quickly, in real-time. The problem lies in connecting the two. The current approach to generating evidence doesn’t scale. Its historic practice – designing trials, gathering data, and publishing papers which are then read, interpreted and slowly absorbed into clinical practice – just won’t cut it. Our evidence base is woefully incomplete and our process for filling its gaps is simply too slow and costly. And of course, we do not have the resources to assess – in any timely way – the coming wave of new treatments.

Carol: Aren’t we really just talking about data then? If I have enough data, don’t I have evidence?

Colin: Chris Anderson, the Editor-in-Chief of Wired magazine, ignited a small firestorm in 2008 when he proposed that “the data deluge makes the scientific method obsolete,” saying that in an age of Big Data, the real challenge was to sift through the data in new ways to find meaningful correlations.

But correlations are not evidence. Healthcare needs evidence, which means knowing which specific treatments produce which specific effects, not just which treatments are associated with them. In fact, as data gets bigger, correlations among data will be everywhere. But I think Chris meant something else. I think he meant that our methods of conducting science will change, not its goals. Making this possible, however, doesn’t come from having lots of data. The breakthrough comes from the work of Judea Pearl.

Carol: How has he changed things?

Colin: Pearl has developed two techniques that underpin artificial intelligence. One is a calculus for handling uncertainty, and the other is a way to use statistical evidence to get beyond mere correlation to cause-and-effect. Together, they change everything.

Without them, reasoning directly from data would be impossible. It’s simply too noisy, with too many biases and confounders. Most people understand that scientists analyze data from experiments, but what they don’t realize is that what makes this ‘reasoning’ possible comes from controlled structure of the experiment itself. Statistics don’t reason, and, without the structure that an experiment provides, don’t produce evidence. Pearl’s work changes all of that by providing a formal mathematics and logic for handing these issues.

Carol: What kinds of things do you see as a result? What does this make possible?

Colin: Pearl’s work, advanced by other luminaries in the field, has already changed the world and given us the foundations for everything from today’s voice recognition to self-driving cars, but I think we’ve yet to realize his biggest contributions. Pearl gave us the mathematics and formal logic for reasoning about cause-and-effect relationships directly from data. By scaling it, we can unleash the real power of real world data, which is what we’re doing at GNS Healthcare.

Someday, we won’t distinguish between controlled vs. natural experiments. Instead, we’ll have ‘always-on’ trials that bridge the research and real world and ‘build-your-own’ trials where people collaborate and contribute data to solve problems they care about. It makes possible an entirely new notion of discovery, and this is really exciting.

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Carol it was a pleasure to participate with you and our fellow panelists in the webinar. I provided a summary of the discussion here (http://bit.ly/HF2lKZ, http://bit.ly/HF2OwR).

Colin raises an important point: old approaches of analysis are insufficient to draw actionable insights from “noisy” information in the real world.

There are two sub-themes here. One relating to the tools. With old tools the signal to noise ratio is often low. Second relating to collaborations. New collaborations will need to explore the white spaces between us.

While the data may fundamentally describe “health”, the dimensions of it are dramatically different. As a result our tools need to adapt. Colin and your team make a great contribution here.

Further, not only do the tools need to be refashioned, how healthcare regulators, financers, delivery providers and manufacturers work together to tackle big data data and analytics needs to be refashioned as well. Some players are exploring new approaches that bust traditional siloes and enlighten shared interests.

The environment we operate in calls for both, new tools and collaborations.

Great interview and fascinating ideas. Good also to see the industry finally adopting the “3V”s of big data over 11 years after Gartner first published them. For future reference, and a copy of the original article I wrote in 2001, see: http://blogs.gartner.com/doug-laney/deja-vvvue-others-claiming-gartners-volume-velocity-variety-construct-for-big-data/. –Doug Laney, VP Research, Gartner, @doug_laney